Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use LizardAPN/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use LizardAPN/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="LizardAPN/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Upload PPO LunarLander-v2 trained agent
Browse files- LunarLander-v2.zip +1 -1
- README.md +1 -1
- replay.mp4 +2 -2
- results.json +1 -1
LunarLander-v2.zip
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README.md
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value:
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name: mean_reward
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verified: false
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---
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 285.85 +/- 20.67
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name: mean_reward
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verified: false
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---
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replay.mp4
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results.json
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{"mean_reward":
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{"mean_reward": 285.8485228534459, "std_reward": 20.667869439955002, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-08-09T10:12:13.767058"}
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